Classification is a supervised learning problem dealing with the prediction of categorical response values for unseen observations using a set of independent features according to training data. \(k\)  nearest neighbours (\(k\) NN) is one of the top-ranked procedures used for classification learning problems [1]}, [2]}, [3]}, [4]}. It is a simple, easy to understand and non-parametric procedure which gives effective results in many cases, especially in large datasets [5]}, [6]}, [7]}, [8]}. The \(k\) NN procedure determines \(k\)  nearest observations in the neighbourhood of a test point using a distance formula and predicts its class by using majority voting. As the \(k\) NN method is simple, easy and outperforms other complex classifiers in many cases, however, \(k\) NN is affected by many data-related problems, such as noise and irrelevant features in the data.
